Skyl Natural Language Processing

derive insights from unstructured text

Build Insightful Text Analysis

Natural language processing plays a critical role in deriving business intelligence from raw business data, including product data, sales and marketing data, customer support, and brand reputation. Text can be an extremely rich source of information, but extracting insights from it can be hard and time-consuming due to its unstructured nature. NLP text analysis will be the key to shifting many legacy companies from data-driven to intelligence-driven platforms, helping humanity quickly get the insights they need to make decisions.

Skyl’s powerful Natural Language Processing platform will let you work with text and help build systems which includes sentiment analysis, entity analysis, entity extraction and content classification.

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Text Classification

Text Classification predicts a set of categories or classes from free-text. Unstructured data in the form of text is everywhere: emails, chats, web pages, social media, support tickets, survey responses, and more. Businesses are turning to text classification for structuring text in a fast and cost-efficient way to enhance decision-making and automate processes in broad applications such as sentiment analysis, topic labeling, spam detection, and intent detection.

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Named Entity Recognition

Text Extraction, also known as Named Entity Recognition (NER), extracts named-entities from text. It can help you locate and classify named entities that are present in unstructured text into predefined categories. These categories can be individuals, organizations, places, cities, quantities and others. It adds a wealth of semantic knowledge to your content and helps you understand the context of any given text. NER has a gigantic usage in areas like powering content recommendations, analyzing customer feedback, tagging textual content on the web, etc.

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